{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LeningClub信贷数据风险分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录：\n",
    "\n",
    "- [一、项目介绍](#一、项目介绍)  \n",
    "- [二、理解数据](#二、理解数据)  \n",
    "- [三、清洗数据](#三、清洗数据)  \n",
    "    - [3.1、去除无关列](#3.1、去除无关列)  \n",
    "    - [3.2、重复值处理](#3.2、重复值处理)  \n",
    "    - [3.3、缺失值处理](#3.3、缺失值处理)  \n",
    "    - [3.4、数据类型转换](#3.4、数据类型转换)  \n",
    "    - [3.5、数据排序](#3.5、数据排序)  \n",
    "- [四、分析数据](#四、分析数据)  \n",
    "    - [4.1、单维变量分析](#4.1、单维变量分析)   \n",
    "    - [4.2、多维变量分析](#4.2、多维变量分析)  \n",
    "- [五、结论与建议](#五、结论与建议)  \n",
    "    - [5.1、LendingClub的业务特点](#5.1、LendingClub的业务特点)  \n",
    "    - [5.2、对该平台的建议](#5.2、对该平台的建议)  \n",
    "    - [5.3、本次项目需要改进的地方](#5.3、本次项目需要改进的地方)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、项目介绍\n",
    "\n",
    "Lending Club 创立于2006年，是全球最大的撮合借款人和投资人的线上金融平台，它利用互联网模式建立了一种比传统银行系统更有效率的、能够在借款人和投资人之间自由配置资本的机制。这种模式就是目前为人熟知的P2P。\n",
    "\n",
    "P2P即基于互联网平台的网络借贷行为。P2P公司充当中介作用，沟通投资人（资金充裕方）和借款人（资金短缺方），提高资金融通的效率，减少信息不对称，降低了小额借贷的难度。这种模式带来便利的同时无形中亦增加了借款人拖欠借款或欠款不还的风险，因此合理地评估借款人信用等级并进行利率定价就显得尤为重要。\n",
    "\n",
    "注：数据集是 LendingClub 平台发生借贷的业务数据（2017年第三季度），具体数据集可以从 [LendingClub 官网](https://www.lendingclub.com/info/download-data.action) 下载。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本项目基于python，尝试对Lending Club平台的贷款数据进行分析，以了解该平台的业务特性及利率定价，并根据该平台的经营情况，给出有针对性的建议。\n",
    "\n",
    "主要对以下问题进行探讨：\n",
    "- LendingClub 贷款的状态是怎样的，违约的比例有多少？\n",
    "- 贷款人的风险等级是怎样分布的，高风险等级的客户多不多？\n",
    "- 人们一般会在 LendingClub 上贷款多大金额？\n",
    "- LendingClub 的客户主要出于什么目的选择贷款？\n",
    "- 在 LendingClub 上贷款的利率是多少，什么利率的人数最多？\n",
    "- LendingClub 是否会给有违约经历的人贷款？\n",
    "- 不同州的人进行贷款的金额有没有不同？\n",
    "- 贷款期限、信用评级、贷款用途、贷款金额和与贷款利率有什么关系？哪个变量的影响比较大？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、理解数据\n",
    "\n",
    "导入相关数据分析及可视化包："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入需要的包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  # 将图片内嵌在交互窗口\n",
    "plt.style.use('ggplot')  # 风格设置近似R这种的ggplot库\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# pd.set_option('display.max_columns', None)  # 显示df所有列\n",
    "pd.set_option('display.max_rows', None)  # 显示df所有行\n",
    "\n",
    "# 忽略弹出的warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入 LendingClub 贷款数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"LoanStats_2017Q3.csv\", skiprows=1, low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先预览基本内容，查看该数据集的相关信息，Pandas为我们提供很多可以方便查看和检查数数据的方法，有 `df.head(n)`、`df.tail(n)`、`df.shape()`、`df.info()`、`df.dtypes` 等 。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 122703 entries, 0 to 122702\n",
      "Columns: 145 entries, id to settlement_term\n",
      "dtypes: float64(107), object(38)\n",
      "memory usage: 135.7+ MB\n",
      "None\n",
      "(122703, 145)\n"
     ]
    }
   ],
   "source": [
    "# 查看行索引和列名\n",
    "# print(df.index)\n",
    "# print(df.columns)\n",
    "\n",
    "print(df.info())\n",
    "\n",
    "# 查看数据所包含行数及列数\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99%</td>\n",
       "      <td>1062.71</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>219.26</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>501.17</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.21%</td>\n",
       "      <td>1022.12</td>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.05%</td>\n",
       "      <td>952.65</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 145 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  NaN        NaN    32000.0      32000.0          32000.0   36 months   \n",
       "1  NaN        NaN     7000.0       7000.0           7000.0   36 months   \n",
       "2  NaN        NaN    16000.0      16000.0          16000.0   36 months   \n",
       "3  NaN        NaN    33000.0      33000.0          33000.0   36 months   \n",
       "4  NaN        NaN    40000.0      40000.0          40000.0   60 months   \n",
       "\n",
       "  int_rate  installment grade sub_grade  ... hardship_payoff_balance_amount  \\\n",
       "0   11.99%      1062.71     B        B5  ...                            NaN   \n",
       "1    7.97%       219.26     A        A5  ...                            NaN   \n",
       "2    7.97%       501.17     A        A5  ...                            NaN   \n",
       "3    7.21%      1022.12     A        A3  ...                            NaN   \n",
       "4   15.05%       952.65     C        C4  ...                            NaN   \n",
       "\n",
       "  hardship_last_payment_amount disbursement_method  debt_settlement_flag  \\\n",
       "0                          NaN                Cash                     N   \n",
       "1                          NaN                Cash                     N   \n",
       "2                          NaN                Cash                     N   \n",
       "3                          NaN                Cash                     N   \n",
       "4                          NaN                Cash                     N   \n",
       "\n",
       "  debt_settlement_flag_date settlement_status settlement_date  \\\n",
       "0                       NaN               NaN             NaN   \n",
       "1                       NaN               NaN             NaN   \n",
       "2                       NaN               NaN             NaN   \n",
       "3                       NaN               NaN             NaN   \n",
       "4                       NaN               NaN             NaN   \n",
       "\n",
       "  settlement_amount  settlement_percentage  settlement_term  \n",
       "0               NaN                    NaN              NaN  \n",
       "1               NaN                    NaN              NaN  \n",
       "2               NaN                    NaN              NaN  \n",
       "3               NaN                    NaN              NaN  \n",
       "4               NaN                    NaN              NaN  \n",
       "\n",
       "[5 rows x 145 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看前5行\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sep-2017' 'Aug-2017' 'Jul-2017' nan]\n"
     ]
    }
   ],
   "source": [
    "isd = df['issue_d'].unique()\n",
    "print(isd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初步观察得出，这是 LendingClub 平台在2017年7,8,9三个月的信贷数据。这份数据一共 122703 行，145 列。数值类型数据（float）共计 107 列，非数值类型数据（object）共计 38 列。由于数据所涉及列数太多，后文我们考虑根据分析需求选择子集进行分析。\n",
    "\n",
    "接下来使用了Numpy，Pandas 科学计算包进行数据清洗，使用 Matplotlib 可视化包进行数据可视化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、清洗数据\n",
    "\n",
    "在这个阶段，主要的目标是合理地检查并处理数据。\n",
    "\n",
    "例如：\n",
    "- 如果数据有唯一的标记符，是否真的只有一个？\n",
    "- 数据是什么类型，是否合理？\n",
    "- 每个字段是否对于分析问题有意义？\n",
    "- 数据应该怎么调整才能适用于接下来的分析和挖掘？\n",
    "- 数据集是否存在异常值？\n",
    "\n",
    "常用的数据清洗工作包括：选择子集（比如去除无关列）、列名重命名、删除重复数据，缺失数据处理、一致化处理（比如数据类型转换，对列数据统一命名，分列）、数据排序、异常值处理。\n",
    "\n",
    "### 3.1、去除无关列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于本数据集的列太多，我们初步聚焦11个重要的变量展开分析，特别是我们最为关注的贷款属性变量：\n",
    "\n",
    "- loan_amnt：贷款金额\n",
    "- term：贷款期限\n",
    "- int_rate：贷款利率\n",
    "- grade：贷款人风险等级\n",
    "- issue_d：申请贷款日期\n",
    "- addr_state：贷款人所在州\n",
    "- loan_status：贷款状态\n",
    "- purpose：贷款用途\n",
    "- delinq_2yrs：逾期次数\n",
    "- annual_inc：贷款人年收入\n",
    "- emp_length：贷款人工作年限\n",
    "- home_ownership：房屋拥有的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99%</td>\n",
       "      <td>B</td>\n",
       "      <td>Sep-2017</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Current</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2.0</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>A</td>\n",
       "      <td>Sep-2017</td>\n",
       "      <td>IL</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>A</td>\n",
       "      <td>Sep-2017</td>\n",
       "      <td>VA</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79077.0</td>\n",
       "      <td>5 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.21%</td>\n",
       "      <td>A</td>\n",
       "      <td>Sep-2017</td>\n",
       "      <td>TX</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.05%</td>\n",
       "      <td>C</td>\n",
       "      <td>Sep-2017</td>\n",
       "      <td>TX</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>9 years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term int_rate grade   issue_d addr_state loan_status  \\\n",
       "0    32000.0   36 months   11.99%     B  Sep-2017         NJ     Current   \n",
       "1     7000.0   36 months    7.97%     A  Sep-2017         IL     Current   \n",
       "2    16000.0   36 months    7.97%     A  Sep-2017         VA     Current   \n",
       "3    33000.0   36 months    7.21%     A  Sep-2017         TX     Current   \n",
       "4    40000.0   60 months   15.05%     C  Sep-2017         TX     Current   \n",
       "\n",
       "              purpose  delinq_2yrs  annual_inc emp_length  \n",
       "0         credit_card          2.0    155000.0  10+ years  \n",
       "1  debt_consolidation          0.0     32000.0  10+ years  \n",
       "2  debt_consolidation          0.0     79077.0    5 years  \n",
       "3  debt_consolidation          0.0    107000.0   < 1 year  \n",
       "4  debt_consolidation          0.0    120000.0    9 years  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据需求选择子集\n",
    "used_col = ['loan_amnt', 'term', 'int_rate', 'grade', 'issue_d', 'addr_state', \n",
    "            'loan_status', 'purpose', 'delinq_2yrs', 'annual_inc', 'emp_length']\n",
    "df = df[used_col]\n",
    "\n",
    "# 查看前五行数据\n",
    "df.head()\n",
    "\n",
    "# 查看第一行数据\n",
    "# df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2、重复值处理\n",
    "\n",
    "先查看数据集中的重复率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007579276790298526"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.shape[0] - df.drop_duplicates().shape[0]) / (df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集中有差不多 0.76% 的重复数据，直接删除即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(122610, 11)\n"
     ]
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3、缺失值处理\n",
    "\n",
    "对于缺失数据，一般有这么几种处理方法：\n",
    "\n",
    "(0)若某列缺失数据占比很大，则直接删除该列；\n",
    "\n",
    "(1)若某列缺失数据占比很小，则需要考虑对缺失值进行删除还是填充；\n",
    "\n",
    "(2)如果是\"float64\"类型，可以用这一列的平均值填充；\n",
    "\n",
    "(3)如果是\"Object\"数据，可以用这一列出现次数最多的值填充，或用\"Unknown\"填充；\n",
    "\n",
    "(4)使用模型预测缺失值；\n",
    "\n",
    "(5)根据业务知识和对指标的理解，自行填充缺失值。\n",
    "\n",
    "\n",
    "对于数据值型数据缺失问题，主要有两种处理方法，具体选择哪种处理思路要根据数据意义及缺失比例灵活选择：\n",
    "\n",
    "- 删除：drop方法\n",
    "- 填充：fillna方法（填充0、均值、中位数、众数、上下数据）\n",
    "\n",
    "先查看每列数据的缺失率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt      0.000008\n",
       "term           0.000008\n",
       "int_rate       0.000008\n",
       "grade          0.000008\n",
       "issue_d        0.000008\n",
       "addr_state     0.000008\n",
       "loan_status    0.000008\n",
       "purpose        0.000008\n",
       "delinq_2yrs    0.000008\n",
       "annual_inc     0.000008\n",
       "emp_length     0.070598\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(df.isnull().any())\n",
    "df.apply(lambda x: sum(x.isnull()) / len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，工作年限emp_length缺失数据较多，但由于样本量比较大，缺失数据比例为 7% 左右。因此直接删除缺失部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113954, 11)\n"
     ]
    }
   ],
   "source": [
    "# 删除含有缺失值的行\n",
    "df = df.dropna(axis=0, how='any')\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4、数据类型转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt      float64\n",
       "term            object\n",
       "int_rate        object\n",
       "grade           object\n",
       "issue_d         object\n",
       "addr_state      object\n",
       "loan_status     object\n",
       "purpose         object\n",
       "delinq_2yrs    float64\n",
       "annual_inc     float64\n",
       "emp_length      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各列的数据类型\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此时利率 int_rate 是 object 类型。为了方便后续分析，使用 `astype` 方法将利率 int_rate 转换为数值类型数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt      float64\n",
       "term            object\n",
       "int_rate       float64\n",
       "grade           object\n",
       "issue_d         object\n",
       "addr_state      object\n",
       "loan_status     object\n",
       "purpose         object\n",
       "delinq_2yrs    float64\n",
       "annual_inc     float64\n",
       "emp_length      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 先使用str.replace方法将字符串中的“ % ”去掉，再使用astype\n",
    "df.int_rate = df.int_rate.str.replace('%',' ').astype('float')\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     7\n",
       "float64    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分类统计数据类型\n",
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5、数据排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113954, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>113949</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32</td>\n",
       "      <td>A</td>\n",
       "      <td>Jul-2017</td>\n",
       "      <td>CA</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>3 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113950</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32</td>\n",
       "      <td>A</td>\n",
       "      <td>Jul-2017</td>\n",
       "      <td>FL</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113951</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32</td>\n",
       "      <td>A</td>\n",
       "      <td>Aug-2017</td>\n",
       "      <td>NV</td>\n",
       "      <td>Current</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113952</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32</td>\n",
       "      <td>A</td>\n",
       "      <td>Sep-2017</td>\n",
       "      <td>TN</td>\n",
       "      <td>Current</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113953</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32</td>\n",
       "      <td>A</td>\n",
       "      <td>Jul-2017</td>\n",
       "      <td>NC</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_amnt        term  int_rate grade   issue_d addr_state  \\\n",
       "113949    10000.0   36 months      5.32     A  Jul-2017         CA   \n",
       "113950    12600.0   36 months      5.32     A  Jul-2017         FL   \n",
       "113951    10000.0   36 months      5.32     A  Aug-2017         NV   \n",
       "113952    15000.0   36 months      5.32     A  Sep-2017         TN   \n",
       "113953    17500.0   36 months      5.32     A  Jul-2017         NC   \n",
       "\n",
       "       loan_status             purpose  delinq_2yrs  annual_inc emp_length  \n",
       "113949     Current  debt_consolidation          0.0     70000.0    3 years  \n",
       "113950     Current  debt_consolidation          0.0     80000.0   < 1 year  \n",
       "113951     Current    home_improvement          0.0    115000.0  10+ years  \n",
       "113952     Current    home_improvement          0.0     45000.0   < 1 year  \n",
       "113953     Current  debt_consolidation          0.0     48000.0   < 1 year  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据贷款比率从高到低进行排序\n",
    "df = df.sort_values(by='int_rate', ascending=False)\n",
    "\n",
    "# 经过上述操作后索引变得不连续，所以使用reset_index重置索引\n",
    "df = df.reset_index(drop=True)\n",
    "print(df.shape)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**到这里，数据清洗工作完成，使用describe方法概览数据**\n",
    "\n",
    "对DataFrame使用describe方法，数值类型的对象将返回一系列描述统计值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>annual_inc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>113954.000000</td>\n",
       "      <td>113954.000000</td>\n",
       "      <td>113954.000000</td>\n",
       "      <td>1.139540e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>14807.946189</td>\n",
       "      <td>13.454196</td>\n",
       "      <td>0.361979</td>\n",
       "      <td>8.147998e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>9676.642372</td>\n",
       "      <td>5.357649</td>\n",
       "      <td>0.975158</td>\n",
       "      <td>3.338759e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>5.320000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>7200.000000</td>\n",
       "      <td>9.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.800000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12.620000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.783950e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>16.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.600000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>30.990000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.100000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           loan_amnt       int_rate    delinq_2yrs    annual_inc\n",
       "count  113954.000000  113954.000000  113954.000000  1.139540e+05\n",
       "mean    14807.946189      13.454196       0.361979  8.147998e+04\n",
       "std      9676.642372       5.357649       0.975158  3.338759e+05\n",
       "min      1000.000000       5.320000       0.000000  0.000000e+00\n",
       "25%      7200.000000       9.930000       0.000000  4.800000e+04\n",
       "50%     12000.000000      12.620000       0.000000  6.783950e+04\n",
       "75%     20000.000000      16.020000       0.000000  9.600000e+04\n",
       "max     40000.000000      30.990000      28.000000  1.100000e+08"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将清洗过的数据进行保存："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('CleanLoans_2017q3.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、分析数据\n",
    "\n",
    "### 4.1、单维变量分析\n",
    "\n",
    "首先进行单维变量分析，对2017年第三季度**贷款状态分布、贷款金额分布、贷款期限分布、贷款用途分布、客户信用等级分布、贷款利率分布、违约次数分布**进行分析，并对结果进行可视化，从而研究 LendingClub 平台贷款的业务特性。\n",
    "\n",
    "#### 1 ）贷款状态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Current', 'Fully Paid', 'In Grace Period', 'Late (31-120 days)',\n",
       "       'Late (16-30 days)', 'Charged Off'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看该列有哪些值\n",
    "df.loan_status.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这些值不是很直观，为了更方便分析，将六种贷款状态进行分类，分为**正常(=0)**和**违约(=1)**，贷款状态分类依据主要参考 [The 10 loan status variants explained](https://help.bitbond.com/article/20-the-10-loan-status-variants-explained)。\n",
    "\n",
    "贷款状态分为7种：\n",
    "\n",
    "- Current：正在还款中\n",
    "- Fully Paid：已还清\n",
    "- In Grace Period：宽限期\n",
    "- Late (16-30 days)：逾期16-30天\n",
    "- Late (31-120 days)：逾期31-120天\n",
    "- Default：违约\n",
    "- Charged Off：坏账\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_status = pd.Series(df[\"loan_status\"], copy=True)\n",
    "codeDict = {'Current':0,'Fully Paid':0,'In Grace Period':1,'Late (31-120 days)':1,'Late (16-30 days)':1,'Charged Off':1}\n",
    "\n",
    "for key, value in codeDict.items():\n",
    "    loan_status.replace(key, value, inplace=True)\n",
    "\n",
    "df[\"loan_status_coded\"] = loan_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    111899\n",
       "1      2055\n",
       "Name: loan_status_coded, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.loan_status_coded.value_counts()\n",
    "pd.value_counts(df[\"loan_status_coded\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "label_list = [\"no default\", \"default\"]    # 各部分标签\n",
    "size = [111845, 2055]    # 各部分大小\n",
    "color = [\"#87CEFA\", \"#7FFFAA\"]     # 各部分颜色\n",
    "explode = [0.05, 0]   # 各部分突出值\n",
    "\"\"\"\n",
    "绘制饼图\n",
    "explode：设置各部分突出\n",
    "label:设置各部分标签\n",
    "labeldistance:设置标签文本距圆心位置，1.1表示1.1倍半径\n",
    "autopct：设置圆里面文本\n",
    "shadow：设置是否有阴影\n",
    "startangle：起始角度，默认从0开始逆时针转\n",
    "pctdistance：设置圆内文本距圆心距离\n",
    "返回值\n",
    "l_text：圆内部文本，matplotlib.text.Text object\n",
    "p_text：圆外部文本\n",
    "\"\"\"\n",
    "patches, l_text, p_text = plt.pie(size, explode=explode, colors=color, labels=label_list, labeldistance=1.1, autopct=\"%1.1f%%\", shadow=False, startangle=90, pctdistance=0.6)\n",
    "plt.axis(\"equal\")    # 设置横轴和纵轴大小相等，这样饼才是圆的\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，平台贷款发生违约的数量占少数。贷款状态为正常的有111,899个，贷款正常状态占比为98.2%，该平台违约率相对较低。\n",
    "\n",
    "#### 2 ）贷款金额分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    113954.000000\n",
       "mean      14807.946189\n",
       "std        9676.642372\n",
       "min        1000.000000\n",
       "25%        7200.000000\n",
       "50%       12000.000000\n",
       "75%       20000.000000\n",
       "max       40000.000000\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loan_amnt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "plt.figure(figsize=(18, 5))\n",
    "x = np.linspace(1000, 40000, 50, endpoint=True)\n",
    "plt.title('Loan amount distribution', fontsize='15')\n",
    "n, bins, patches = plt.hist(df['loan_amnt'], color='green', bins=x)\n",
    "plt.tick_params(axis='both', labelsize=10)  # 调整坐标字体大小\n",
    "\n",
    "mu =np.mean(df['loan_amnt']) #计算均值\n",
    "sigma =np.std(df['loan_amnt'])\n",
    "\n",
    "# plt.figure(figsize=(18, 3))\n",
    "y = norm.pdf(bins, mu, sigma)*0.15e9  # 拟合一条最佳正态分布曲线y\n",
    "plt.plot(bins, y, 'r--')  # 绘制y的曲线\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "平台贷款呈现右偏正态分布，贷款金额最小值为1,000美元，最大值为40,000美元，中位数为12,000美元，平均数为14807.9美元。\n",
    "\n",
    "可以看出平台的贷款业务主要以小额贷款为主，这有利于分散风险，手续简单，放贷过程快，贷款范围较广。\n",
    "\n",
    "#### 3 ）贷款期限分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>term</td>\n",
       "      <td>113954</td>\n",
       "      <td>2</td>\n",
       "      <td>36 months</td>\n",
       "      <td>81370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>grade</td>\n",
       "      <td>113954</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>39117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>issue_d</td>\n",
       "      <td>113954</td>\n",
       "      <td>3</td>\n",
       "      <td>Aug-2017</td>\n",
       "      <td>40526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>addr_state</td>\n",
       "      <td>113954</td>\n",
       "      <td>49</td>\n",
       "      <td>CA</td>\n",
       "      <td>15240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>loan_status</td>\n",
       "      <td>113954</td>\n",
       "      <td>6</td>\n",
       "      <td>Current</td>\n",
       "      <td>108002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>purpose</td>\n",
       "      <td>113954</td>\n",
       "      <td>12</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>64490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>emp_length</td>\n",
       "      <td>113954</td>\n",
       "      <td>11</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>39113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count unique                 top    freq\n",
       "term         113954      2           36 months   81370\n",
       "grade        113954      7                   C   39117\n",
       "issue_d      113954      3            Aug-2017   40526\n",
       "addr_state   113954     49                  CA   15240\n",
       "loan_status  113954      6             Current  108002\n",
       "purpose      113954     12  debt_consolidation   64490\n",
       "emp_length   113954     11           10+ years   39113"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['object']).describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    81370\n",
       " 60 months    32584\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df[\"term\"])  # 分类统计贷款期限"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "label_list = [\"36 months\", \"60 months\"]    # 各部分标签\n",
    "size = [81331, 32569]    # 各部分大小\n",
    "color = [\"#87CEFA\", \"#7FFFAA\"]     # 各部分颜色\n",
    "explode = [0, 0]   # 各部分突出值\n",
    "\n",
    "patches, l_text, p_text = plt.pie(size, explode=explode, colors=color, labels=label_list, labeldistance=1.1, autopct=\"%1.1f%%\", shadow=False, startangle=90, pctdistance=0.6)\n",
    "plt.axis(\"equal\")    # 设置横轴和纵轴大小相等，这样饼才是圆的\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lending Club 平台有两种贷款期限：36 months和60 months，其中以36 months期限的贷款为主，占比为71.4%。一般来说，贷款期限越短，违约风险越低，贷款期限越长，不确定性越大，违约风险更高，由此看出 LendingClub 有意通过控制期限来控制违约风险。\n",
    "\n",
    "#### 4 ）贷款用途分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    64490\n",
       "credit_card           22255\n",
       "home_improvement       8894\n",
       "other                  8230\n",
       "major_purchase         2923\n",
       "medical                1727\n",
       "car                    1382\n",
       "small_business         1224\n",
       "vacation               1104\n",
       "moving                 1021\n",
       "house                   634\n",
       "renewable_energy         70\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purpose'].value_counts() # 按借款用途统作统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 5))\n",
    "bins = list(df['purpose'].value_counts().index)\n",
    "values = list(df['purpose'].value_counts().values)\n",
    "\n",
    "plt.title('Purpose', fontsize='15')\n",
    "plt.xticks(rotation=30)\n",
    "plt.bar(x=bins, height=values, color='green', width=0.3)\n",
    "plt.tick_params(axis='both', labelsize=12)  # 调整横坐标字体大小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图贷款目的的最大频数统计可得，2017年第三季度该平台大部分贷款目的为债务重组（借新债还旧债），其次是信用卡还款，第三是住房改善。\n",
    "\n",
    "一般来说，贷款用途为债务重组和信用卡还款的客户现金流较为紧张，此类客户是在传统银行渠道无法贷款，才转来P2P平台贷款，这部分客户的偿还贷款能力较弱，发生违约的可能性较高。\n",
    "\n",
    "#### 5 ）客户信用等级分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    39117\n",
       "B    33517\n",
       "A    19516\n",
       "D    13710\n",
       "E     5062\n",
       "G     1595\n",
       "F     1437\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "label_list = list(df['grade'].value_counts().index)    # 各部分标签\n",
    "size = list(df['grade'].value_counts().values)    # 各部分大小\n",
    "\n",
    "patches, l_text, p_text = plt.pie(size, labels=label_list, labeldistance=1.03, autopct=\"%1.1f%%\", shadow=False, startangle=90, pctdistance=1.2)\n",
    "plt.axis(\"equal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2017年第三季度C等级的客户最多，绝大多数用户的信用等级为 A~C，三者合计占比为80.8%。此外信用等级为 E~G 类的客户占比仅为7.1%。\n",
    "\n",
    "随着贷款风险等级的提高，贷款人数在不断减少，说明虽然风险较低的贷款人贷款利率较低，但平台还是还是选择少给他们贷款以规避风险。\n",
    "\n",
    "#### 6 ）贷款利率分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    113954.000000\n",
       "mean         13.454196\n",
       "std           5.357649\n",
       "min           5.320000\n",
       "25%           9.930000\n",
       "50%          12.620000\n",
       "75%          16.020000\n",
       "max          30.990000\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.int_rate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 5))\n",
    "x = np.linspace(5, 30, 50, endpoint=True)\n",
    "plt.title('Interest Rate distribution', fontsize='15')\n",
    "n, bins, patches = plt.hist(df['int_rate'], color='green', bins=x)\n",
    "plt.tick_params(axis='both', labelsize=10)  # 调整坐标字体大小\n",
    "\n",
    "mu =np.mean(df['int_rate']) #计算均值\n",
    "sigma =np.std(df['int_rate'])\n",
    "\n",
    "# plt.figure(figsize=(18, 3))\n",
    "y = norm.pdf(bins, mu, sigma)*1e5  # 拟合一条最佳正态分布曲线y\n",
    "plt.plot(bins, y, 'r--')  # 绘制y的曲线\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lending Club平台贷款利率呈现右偏正态分布，利率最大为30.99%，最小为5.32%，中位数为12.62%，平均值为13.45%，说明P2P的利率还是高于银行利率的。利率是资金的价格，利率越高，借款人借贷成本越高，借款人违约的可能性越高。\n",
    "\n",
    "#### 7 ）违约次数分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([90005, 15451,  4765,  1821,   809,   417,   258,   153,    84,\n",
       "          58,    33,    28,    26,    10,    10,     9,     7,     2,\n",
       "           2,     2,     1,     1,     1,     1])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 根据 delinq_2yrs 列进行分组并求平均值\n",
    "# rate_mean = df.groupby('delinq_2yrs').count().values\n",
    "# purposes = df.groupby('delinq_2yrs').count().index\n",
    "\n",
    "df['delinq_2yrs'].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAF2CAYAAACbGWRxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAZ3ElEQVR4nO3dfZRdVZnn8W8gvCaEBpEXX2JE4aGMTeiOLd1MO/KmIktXq3ExDLQGERtxHBVhEBxQxMm0QIOucYRWpBl8QdQWEBub7kZRwfYFSgWJxUNLgAANMbQxkoIkQDJ/7FNwrWQnt24l9yV+P2vVyr1nn31rn5N77u+cvfe5NWXt2rVIkrQ+W/W6AZKk/mVISJKqDAlJUpUhIUmqMiQkSVVTe92ATW14eNjpWpLUgblz504Zv2yLCwmAuXPnbvLXHBkZYWhoaJO/bjcN+jbY/t4b9G2w/XXDw8PrXW53kySpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlS1Rb5VeGdWrRsEYuXL15v2eiaUZbcu6Rad+bOM9l7l703V9MkqScMiRaLly/mkMsP6ajujfNvNCQkbXHsbpIkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUtXUdlaKiFnARcCfAauAvwfel5lPRsQBwKXAEDACvD0zf9bUmwJ8DDiheanPAqdn5tqmvOO6kqTNr90riYuAXwF7AQcArwTeFRHbAl8HvgDsAlwOfL1ZDvBXwBuAOcD+wOuBEwEmU1eS1B3thsQLga9k5srMfBi4HpgNHEy5GvlEZq7KzP8DTAEOberNBy7IzAcy80HgAuC4pmwydSVJXdBWdxPwCeDoiPgO5az/tcBZlKC4fVwX0O3N8rEgua2l7LZmGZOsu0EjIyNtbdR4o2tGO6oHMDo62vHv7ZaVK1f2fRs3xPb33qBvg+2fuHZD4nuU7p/fAltTuoauAc4Elo9bdzmwU/N4+rjy5cD0ZrxhfFnbdTc2LjE0NNTGJq1ryb1LOqoHMG3aNIZmdfZ7u2VkZKTjfdMPbH/vDfo22P664eHh9S7faHdTRGxFObO/CpgG7Ea5mjgXWAHMGFdlBvBo83h8+QxgRfMhP5m6kqQuaGdMYldgJvB/m7GD/wAuA44EFgL7N1cGY/ZvltP8O6elbM64sk7rSpK6YKPdTZn5SETcA5wUEX9D6QaaTxk/+A7wFPCeiPhb4B1NtW83/34OeH9EfBNYC5wCfLIpm0xdSVIXtDu76U3AEcBS4JfAE8DJmbmaMk31rcBvgOOBNzTLAT4NfAP4OXAHcF2zjMnUlSR1R1sD180NbgdXyn4KzK2UrQVOa342aV1J0ubn13JIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqmtruihFxNPBhYCbwMHBcZt4UEYcBn2qW/6hZfl9TZzvgYuDNwGPAeZl5YctrdlxXkrT5tXUlERGvAs4F3gbsBPxnYFFE7AZcBZwF7ArcCny5perZwD7AC4BDgNMi4ojmNTuuK0nqjna7mz4CnJOZP8zMNZn5YGY+CLwJWJiZX83MlZQP9jkRsV9Tbz7w0cxclpkjwCXAcU3ZZOpKkrpgo91NEbE18DLg2oj4JbA9cA3wP4DZwG1j62bmaETcDcyOiCXAXq3lzeM3NI8nU3eDRkZG2lltHaNrRjuqBzA6Otrx7+2WlStX9n0bN8T2996gb4Ptn7h2xiT2ALahjA28AngC+DpwJjAdWDpu/eWULqnpLc/HlzHJuhs0NDTUzmrrWHLvko7qAUybNo2hWZ393m4ZGRnpeN/0A9vfe4O+Dba/bnh4eL3L2+luerz595OZ+VBmPgJcCBwJrABmjFt/BvBoU8a48rEyJllXktQFGw2JzFwGPACsbVk89nghMGdsYURMA15EGWtYBjzUWt48XrgJ6kqSuqDdKbCXAf89Iq6ndDedDPwDcDVwfkTMA64DPgTcnpl3NvU+B5wZEbdSuq3eQZkhxSTrSpK6oN3ZTR8FbgHuAkaAnwILMnMpMA9YACwDDgSObqn3YeBu4D7gu8D5mXk9wGTqSpK6o60ricx8AnhX8zO+7AZgv3UqlbJVwPHNz/rKO64rSdr8/FoOSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVVMnsnJE7AP8HPj7zPzLZtkxwF8DuwH/Ahyfmb9uynYFLgVeDTwCnJGZV7S8Xsd1JUmb30SvJD4F3DL2JCJmA58G3gLsATwGXDRu/dVN2bHAxU2dSdWVJHVH21cSEXE08BvgX4EXN4uPBb6Rmd9r1jkLGImInYA1wDzgpZm5Arg5Iq6lhMLpk6wrSeqCtkIiImYA5wCHAie0FM2mhAYAmXl3RKwG9qV80D+ZmXe1rH8b8MpNUHeDRkZG2lltHaNrRjuqBzA6Otrx7+2WlStX9n0bN8T2996gb4Ptn7h2ryQ+ClyamQ9EROvy6cDycesuB3YCngJ+WymbbN0NGhoaame1dSy5d0lH9QCmTZvG0KzOfm+3jIyMdLxv+oHt771B3wbbXzc8PLze5RsNiYg4ADgc+KP1FK8AZoxbNgN4lHI1UCubbF1JUhe0M3B9MDALWBwRDwOnAvMi4ifAQmDO2IoRsTewHXBX8zO1mRE1Zk5Th0nWlSR1QTvdTZ8Brmx5fiolNE4Cdgd+EBGvAH5CGbe4KjMfBYiIq4BzIuIE4ADgL4CDmtf54iTqSpK6YKMhkZmPUaanAhARK4CVmbkUWBoR76R84D8LuAF4W0v1dwF/B/wK+A/gpMxc2Lzuwk7rSpK6Y0I30wFk5tnjnl8BrPcmt+bGuDds4LU6ritJ2vz8Wg5JUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVUze2QkRsB1wEHA7sCtwNnJGZ/9iUHwZ8CpgJ/Ag4LjPva6l7MfBm4DHgvMy8sOW1O64rSdr82rmSmArcD7wS2Bk4E/hKRMyKiN2Aq4CzKAFyK/DllrpnA/sALwAOAU6LiCMAJlNXktQdG72SyMxRygf2mH+IiHuAucCzgIWZ+VWAiDgbeCQi9svMO4H5lKuDZcCyiLgEOA64HnjTJOpKkrpgwmMSEbEHsC+wEJgN3DZW1gTK3cDsiNgF2Ku1vHk8u3k8mbqSpC7Y6JVEq4jYBvgicHlm3hkR04Gl41ZbDuwETG95Pr6MprzTuhs0MjLSzmrrGF0z2lE9gNHR0Y5/b7esXLmy79u4Iba/9wZ9G2z/xLUdEhGxFfB5YDXw7mbxCmDGuFVnAI82ZWPPV44rm2zdDRoaGmpntXUsuXdJR/UApk2bxtCszn5vt4yMjHS8b/qB7e+9Qd8G2183PDy83uVtdTdFxBTgUmAPYF5mPtEULQTmtKw3DXgRZaxhGfBQa3nzeOEmqCtJ6oJ2ryQuBoaAwzPz8ZblVwPnR8Q84DrgQ8DtzcAzwOeAMyPiVkrAvAN42yaoK0nqgo1eSUTEC4ATgQOAhyNiRfNzbGYuBeYBC4BlwIHA0S3VP0wZjL4P+C5wfmZeDzCZupKk7mhnCux9wJQNlN8A7FcpWwUc3/xs0rqSpM3Pr+WQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVTeirwtXfFi1bxOLli6vlo2tGq990O3Pnmey9y96bq2mSBpQhsQVZvHwxh1x+SEd1b5x/oyEhaR12N0mSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqDAlJUpUhIUmqMiQkSVWGhCSpypCQJFUZEpKkKkNCklRlSEiSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJUZUhIkqoMCUlSlSEhSaqa2usGSK0WLVvE4uWL11s2umaUJfcuWW/ZzJ1nsvcue2/Opkm/lwwJ9ZXFyxdzyOWHTLjejfNvNCSkzcDuJklSlSEhSaoyJCRJVYaEJKnKkJAkVRkSkqQqQ0KSVGVISJKqvJlO2oQ6vWMcvGtc/cmQkDahTu8YB+8aV3+yu0mSVNXXVxIRsStwKfBq4BHgjMy8oretkqTfH30dEsCngNXAHsABwHURcVtmLuxts6Qt04bGVMBv4u2GfhvX6tuQiIhpwDzgpZm5Arg5Iq4F3gKc3tPGSVuoLWFMZdC/br7f/g/6NiSAfYEnM/OulmW3Aa/sUXskDQC/bn7TmrJ27dpet2G9IuIVwFczc8+WZe8Ajs3Mg2v1hoeH+3ODJKnPzZ07d8r4Zf18JbECmDFu2Qzg0Q1VWt9GSpI6089TYO8CpkbEPi3L5gAOWktSl/RtdxNARFwJrAVOoMxu+iZwkLObJKk7+vlKAuBdwA7Ar4AvAScZEJLUPX19JSFJ6q1+v5KQJPWQISFJqjIkJiEinG7bQ1vC/h/0bbD9Wz5DogMR8RKAzHRApwci4sCI2Kbl+cAd6IO+DYN+DAx6+zckIrZqfT9N9r3lwPUERMQbgYuB+4EHgCsy86u9bdXERMSxwBHAjcAtmfnziNgqM9f0uGkbFRGvB/4O+Pfm51uZ+Te9bdXEDPo2DPoxMOjt35CxMBgLvoj488y8ebKv65VEmyJiJvAB4CTgSGAEODsiDm/K+3pfRsTWEbEAOA+4Ezga+FJEvGhAAuJZwLuB9wAvB64CPhARR/W0YRMw6NuwBRwDA93+jcnMtZm5NiK2j4jzgO9FxOzJvu5A75Qu2x3YFfhRZi4F/hdwLfBZgAH4oN0BOByYl5kLgKOAW4Ev97RV7ZsB7A/cnpmrMvMSYAFwUUTs0tumtW3Qt2HQj4FBb/86xgdbc6V0NfAHwA6b4r4yQ6J9uwIPAjsCZOZjlAN8SkScDH1/JrID8DjwfIDM/A3wV8CLIuJE6Pv2Twd+Ajz9hY+Z+QlgCeXssN/bD4O/DYN+DAx6+5/WjDusr5v4VsrXF70U2K5Zd1JjEgOxQ7opInYbN6A4to/+BZgFvGasrPk7FwuAv4yIbfr8TGQ1sBLYKyK2A8jM1cCHmp++OJOKiJ0rg253UILuP0XEji1VPgC8PSK27Yf2A0TEXhGxfcvzgdqGiJjbdM2M/9AciGMgIl4XEX+ynqKBaH87MnNNZq6JiIMi4tMRcVxERGbeD3wc2AbYrVl3UgPPhkQjImZFxPeBa4BvRMSciNi6+Y/YttnR5wNnRsRzW6o+CCyn5eywVyLiuRHxsuZx6wftVpm5HPgO8GZgZku1zwPLx/pleyUi9omIfwK+CHwtImZBeYNHxNRm/19C6Sb745aq9wCLKGdOPdVsw7eAK4CvRMRceHobth6QbTgKuAV4Lzxz4tDS/r49Bpr9/23gcmCXcWV93/6JiogPUj6vHgVeBVwREftl5vnAtsBRrScrnTIkgIjYgdIvOUz5a3i/Bc4G5jerPAWQmRdR3lAfjIihpmw15UsIH+xik39HREyJiI8Ad1MG4nZvPpjGgmLsTOJcyqX2WyNir2bZDpQ32SNdbXSLiHg78G3gp8BplG6BBWP99Jn5ZPPvlyiD7m+PiLG/KrMDsIoyCNkzEfF+4CbKNhwJTAPOiIgdI2JKZo69h/p2Gxp7Aj8AnhMRR8LTJxn9fgycBvwc+FlmPisz/7m1vN/bvz7N5xIRsc6fdGiOjVcAf5qZp2bmscALKccPwMeAY4GXTLYdhkTxPGB74KLMXEL51tmfUC5BX5yZT4110TRlzwe+HBEXAl+jXMa2fih326spf8nvAuA3wDHwzGXmuDPZjwB/AlwYEc8D/gswBXi4Fw1vvBA4JzNPz8xfUN7cb6QMvgHlTLB5eAblb418MSL+ljKV9/vA6h7ufygnFq9pDtjHgfsoV2yPt0xJHOvG7LttGHdC8e/AvcC8iJjeXE1v3fJ/0I/HQADfzMz3A0TEayPiJRGx09MrPLP/+7H9T2tmJ30XOKbpAnuyWd56pbM9sBfwWEQcHRH/RunOPAuePhlZAZw8rmtzwrxPAoiIfYEfA0OZ+VCzbDblYF6Smac0H7JPNWXPA/4ceBlwbWZ+r0dNp2nPjpTBqjuA9zWPz87MO8YGt1oHuSLiIOD9lLPGZwPzM/OHPWr+2P5clZlLmzCeBtwAHJ+ZP2vWad3/2wOHULbz+5l5U4+a/rSmS+zJKDdpXUnZt1dSrk4XZuatA7ANU4D/SXkfraRM1/1CZl7ZlE9t+cDqt2NgX8r9DyOU/boKWEMJ79Mz88d9fgxPaU7mxo7Xc4EDKVPV/4DS0zGdEmifp4T5pyljLFOBD2Tm1yJiW+DgzPzniPhDYOfJ3ithSDQi4ibgjsw8qXm+NXAicBhwSmbeGxHPbqbO9a1mwO69wOLM/OAG1psCPCcz++YSu+VA+SNKv/LczHyipXyP5kqvb0XEnwJ7ZuY1TXfBAmAP4KOZeWdE7JmZvbxqW6+Wff/XwNisn9OAgygfSOdn5s39fAxExBnAW4HzMvOyiHghZZrrryn7/1d93v7WE7mtgNso41sB3E4Zu3o3pUvtlIi4gBJy/605IdyRcqPmk8A7m0H5SbO76RkfA97YnJGM9WH+AtgHGI2IFwN3x+/+pby+k5m3UPqUXxIRR7SWRcSFEXFgs97afgoI+J1ZGAcDd40LiF2BOyLi5b1oW7sy84dNQExpup2+RvmDWTOabVjYj9vQsu+3Br7dfFjtTzlJei5wS/PeX9THx8AFlKviy5oP3Hsof4fmMEpXXt8ewxHxKkqgjdmJEtInU046LszMq4HPlNXjdZTt/QFlos1FlC7O1cCJmyogwJBodUPz87mWZXc0/25LmX2yd2b+W7cb1q6W/tR/osy9f02z/OCI2IMy7nB/j5q3US193i8Hrm+WnRQRHweeAPbJzB/3qn0TNLYtS5rHv83MXwMv7vNtWA18JiJup1wBXUI5E5/dvPf79hhopnTf0jwd2/9LKV1P21DOxPu1/bsD+0XE+RHxS8oY3T8C/0q5j2O3Zr1vUf6E8zHA6sw8vXl8I3BoZr41M0c3ZcPsbmrRXK7dRvn72jcBb6HcnHJC61ltP2vpNngVpctgDqWf9vXNHOq+1szkuA74LuU7pmZRxiZu6GW7JqLpZnqCsu8/Q3kPva+5suhrETEfeBvw2cz8QjMD6L8C/y8zF/W2de2JMmV9ddNteSlw/Ya6Xntl3BjPsylXBc8HzmymsY51H3+F0vX9rWYSzWGUr3a5PTPP2tztNCTGiYj9gD8DXgv8IDM/3uMmTVhETKNcTfwhcEYz7W8gNINtt1HOwC/IAfryuzFR7lU5j3Lfw7mZeUGPm9S2ZkD9qUE5KVqfZvLAJyndfAsy88IeN6mqOSl6KeW+jgMo01q/0dJltiYiLqFcabwzMx9qBqffC/yy6YLarAyJirEz8l63oxMRMQ84FDh1EM5eWzUHwLsp05FX9ro9nWi6zf4CuC4zV/W6Pb9vmv1/FHB1P7+HIuJ4ysnErZTxn/nA6yizmC4bm5UUEbtTpkh/HLg0M1dFF7+52ZDYAg1ywEm/D5oxwquA/52Z10W5e3pfSjflVOCezDynZf1TgdmUq4munngYEpLUZRFxDHBaZh7QPN+Kco/KnpSb4PbjmS/reyAz39ertjq7SZK67w5gz4h4Djz9HVk/pNz3cDHlK/wPBe7vZUCAISFJvXAfZTrr2S3LfkH5uo1VmXkF8NrMPLkHbfsddjdJUg9ExCso01uvAW4GTqFMg33P2NeH9ANDQpJ6JCIOpdwP9MeU2XB9N+XekJCkHuvmlNaJMiQkSVUOXEuSqgwJSVKVISFJqjIkJElVhoQkqcqQkCRVGRKSpCpDQpJU9f8BYsxcllTKm7wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "labels = list(df['delinq_2yrs'].value_counts().index)\n",
    "labels = labels[:6] + ['other']\n",
    "labels = [str(x) for x in labels]\n",
    "values = list(df['delinq_2yrs'].value_counts().values)\n",
    "values = values[:6] + [sum(values[6:])]\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "plt.bar(x=labels, height=values, color='green', width=0.3)\n",
    "plt.tick_params(axis='both', labelsize=12)  # 调整坐标字体大小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在贷款客户当中，绝大多数客户都没有过违约经历，说明该平台对客户是否违约非常看重，通过这个指标也可以更好地降低客户违约风险。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2、多维变量分析\n",
    "\n",
    "接下来尝试进行多维变量分析，探索变量之间的关系：\n",
    "- 贷款金额与所属州的关系\n",
    "- 贷款期限与贷款利率的关系\n",
    "- 信用评级与贷款利率的关系\n",
    "- 贷款用途与贷款利率的关系\n",
    "- 贷款金额与贷款利率的关系\n",
    "- 违约次数与贷款利率的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>loan_status_coded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>30.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Aug-2017</td>\n",
       "      <td>CA</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2 years</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>30.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Jul-2017</td>\n",
       "      <td>CA</td>\n",
       "      <td>Current</td>\n",
       "      <td>small_business</td>\n",
       "      <td>2.0</td>\n",
       "      <td>155200.0</td>\n",
       "      <td>2 years</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>30.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Sep-2017</td>\n",
       "      <td>GA</td>\n",
       "      <td>Current</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>2 years</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>30.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Aug-2017</td>\n",
       "      <td>CA</td>\n",
       "      <td>Current</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>28825.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>30.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Aug-2017</td>\n",
       "      <td>GA</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>6 years</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate grade   issue_d addr_state loan_status  \\\n",
       "0    20000.0   60 months     30.99     G  Aug-2017         CA     Current   \n",
       "1    32000.0   60 months     30.99     G  Jul-2017         CA     Current   \n",
       "2    11500.0   60 months     30.99     G  Sep-2017         GA     Current   \n",
       "3    28000.0   60 months     30.99     G  Aug-2017         CA     Current   \n",
       "4    28825.0   60 months     30.99     G  Aug-2017         GA     Current   \n",
       "\n",
       "              purpose  delinq_2yrs  annual_inc emp_length  loan_status_coded  \n",
       "0  debt_consolidation          0.0     50000.0    2 years                  0  \n",
       "1      small_business          2.0    155200.0    2 years                  0  \n",
       "2               other          0.0     28800.0    2 years                  0  \n",
       "3         credit_card          0.0    125000.0    3 years                  0  \n",
       "4  debt_consolidation          1.0     80000.0    6 years                  0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bak = df.copy()\n",
    "df_bak.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 ）贷款金额与所属州的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>addr_state</th>\n",
       "      <th>loan_amnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AK</td>\n",
       "      <td>3897200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>AL</td>\n",
       "      <td>19563200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AR</td>\n",
       "      <td>11463775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>AZ</td>\n",
       "      <td>39637375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>CA</td>\n",
       "      <td>230786450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  addr_state    loan_amnt\n",
       "0         AK    3897200.0\n",
       "1         AL   19563200.0\n",
       "2         AR   11463775.0\n",
       "3         AZ   39637375.0\n",
       "4         CA  230786450.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_by_state = df.groupby(['addr_state'])['loan_amnt'].sum() # 按州统计贷款金额\n",
    "data_group_by_state_df= data_group_by_state.reset_index() # 将结果转为 dataframe\n",
    "data_group_by_state_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.figure(figsize=(15, 5))\n",
    "sns.set_context(\"notebook\", font_scale=1, rc={\"lines.linewidth\": 5})\n",
    "sbarplot = sns.barplot(y='loan_amnt' , x='addr_state' , data=data_group_by_state_df )\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Loan_amount')\n",
    "plt.xticks(rotation=0)\n",
    "plt.title('State VS Loan_amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图可知 Lending Club 的总部在加州（CA），因此加州的市场开拓也相对其他较好。其次是德克萨斯州（TX）和纽约州（NY）。\n",
    "\n",
    "同时，从风险防范角度来看，应重点审核这几个城市贷款申请人的基本信息。\n",
    "\n",
    "#### 2 ）贷款期限、信用评级与贷款利率的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    39117\n",
       "B    33517\n",
       "A    19516\n",
       "D    13710\n",
       "E     5062\n",
       "G     1595\n",
       "F     1437\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grade'].value_counts()  # 查看信用评级的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>term</th>\n",
       "      <th>36 months</th>\n",
       "      <th>60 months</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>6.990394</td>\n",
       "      <td>7.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>10.586379</td>\n",
       "      <td>10.404137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>14.199975</td>\n",
       "      <td>14.558627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>D</td>\n",
       "      <td>19.052016</td>\n",
       "      <td>19.094583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td>24.726547</td>\n",
       "      <td>25.615020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td>30.186412</td>\n",
       "      <td>30.283002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td>30.895873</td>\n",
       "      <td>30.898076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "term    36 months   60 months\n",
       "grade                        \n",
       "A        6.990394    7.970000\n",
       "B       10.586379   10.404137\n",
       "C       14.199975   14.558627\n",
       "D       19.052016   19.094583\n",
       "E       24.726547   25.615020\n",
       "F       30.186412   30.283002\n",
       "G       30.895873   30.898076"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用数据透视表\n",
    "df.pivot_table(index='grade',columns='term',values='int_rate', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于36个月的贷款类型，信用等级为A的客户的平均贷款利率为6.99%，信用等级为G的客户的平均贷款利率为30.89%。说明资信情况越差，贷款利率越高，60个月的贷款分析同理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据 grade 列进行分组并求平均值\n",
    "rate_mean = df.groupby('grade')['int_rate'].mean()\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "labels = list(rate_mean.index)\n",
    "labels = [str(x) for x in labels]\n",
    "values = list(rate_mean.values)\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "plt.bar(x=labels, height=values, color='g', width=0.3)\n",
    "plt.tick_params(axis='both', labelsize=12)  # 调整坐标字体大小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，该平台的利率最高档为30%，而利率最低档为7%左右，总体利率水平也相对传统银行较高。 信用评级从A到G，A的的借款人信用评分最高，财务状况较好，违约发生的可能性较低，因此利率也相对较低。\n",
    "\n",
    "而同时贷款期限长意味着不确定性增加，风险也随之增加，因此期限较长的贷款在同信用等级下的借款利率也相对高。\n",
    "\n",
    "#### 3 ）贷款用途与贷款利率的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n",
    "\n",
    "sboxplot = sns.boxplot(y=\"purpose\", x=\"int_rate\", data=df)\n",
    "sns.despine(top=True)\n",
    "plt.tick_params(axis='both', labelsize=12)  # 调整坐标字体大小\n",
    "plt.xlabel('Interest_Rate')\n",
    "plt.ylabel('Purpose')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "贷款用途分别为house、small_business以及 renewable energy 的贷款利率较高。其中贷款用途为house的贷款利率为最高。\n",
    "\n",
    "#### 4 ）贷款金额与贷款利率的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_amnt = df['loan_amnt']\n",
    "int_rate = df['int_rate']\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.xlabel('loan_amnt')\n",
    "plt.ylabel('int_rate')\n",
    "plt.xticks(rotation=30)\n",
    "plt.title('loan_amnt VS int_rate', fontsize='15')\n",
    "plt.tick_params(axis='both', labelsize=12)  # 调整坐标字体大小\n",
    "plt.scatter(loan_amnt, int_rate, c='r', s=2, alpha=0.7, );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图是贷款金额和利率的线性关系图，从图中并没有发现贷款金额和贷款利率有明显的关系。\n",
    "\n",
    "#### 5 ）违约次数与贷款利率的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据 delinq_2yrs 列进行分组并求平均值\n",
    "rate_mean = df.groupby('delinq_2yrs')['int_rate'].mean()\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "labels = list(rate_mean.index)\n",
    "labels = [str(x) for x in labels]\n",
    "values = list(rate_mean.values)\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "plt.bar(x=labels, height=values, color='g', width=0.3)\n",
    "plt.tick_params(axis='both', labelsize=12)  # 调整坐标字体大小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "违约次数越多的人意味着自身财务状况较差，偿付能力也较低，因此此类客户贷款风险越高，对此部分资产应给予更高的利率定价。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五、结论与建议"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1、LendingClub的业务特点\n",
    "\n",
    "1. LendingClub 贷款以小额贷款为主，这有利于分散资金风险，而且手续简单，放贷过程快；\n",
    "\n",
    "2. 2017年第3季度贷款的违约情况得到了较好的控制，大部分贷款都处于正常偿还阶段，违约率只有1.8%；\n",
    "\n",
    "3. 平台主要有两种贷款类型，36月贷款和60月贷款，36月贷款的人数占71.4%，应该是平台有意通过控制贷款期限来降低违约风险；\n",
    "\n",
    "4. 平台贷款利率平均为13.45%，说明P2P的利率还是高于传统金融机构的利率；\n",
    "\n",
    "5. 大部分贷款目的为债务重组和信用卡还款，这意味着具有更高的违约风险；\n",
    "\n",
    "6. 贷款客户主要是高信用级别客户，基本上都没有违约经历；\n",
    "\n",
    "7. 资信情况越差，违约次数越多的贷款人，其贷款利率越高；而且贷款期限越长，贷款利率也越高，因为贷款期限长意味着不确定性增加。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2、对该平台的建议\n",
    "\n",
    "1. 贷款业务主要集中在加州、德克萨斯州和纽约州，从风险防范角度来看，应重点审核这几个城市贷款申请人的基本信息。\n",
    "\n",
    "2. 要限制高利率贷款的人数，利率越高，借款人还款成本越高，违约的可能性越高，平台不能盲目追求高收益。\n",
    "\n",
    "3. 完善客户画像，根据客户信息确定客户的常见特征，从而确定目标客户群体，完整的客户信息有利于风控人员和系统分析把控违约风险。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3、本次项目需要改进的地方\n",
    "\n",
    "1. 违约率虽然比较低，但是可以对违约人的所属州，房屋拥有情况，工作收入，贷款用途等变量进行分析，从而更准确地分辨出可能违约的人，进一步降低违约风险。\n",
    "\n",
    "2. 增加对贷款人住房情况的分析，因为房屋属于大额资产，贷款人是否持有房子，以及房子是否抵押，对贷款人是否违约的影响还是比较大的。\n",
    "\n",
    "3. 可以借助机器学习技术对贷款数据进行建模，自动进行风险分析，并得到个变量之间更为准确的关系。"
   ]
  }
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